Development Of Battery Management System Research Articles

Estimation of Li-ion battery SOH based on factors combination of FBG wavelength shift and charge-discharge process

For the purpose of daily battery maintenance, safety forecasting, and energy ladder use, it is crucial to accurately estimate the state of health (SOH) of Li-ion batteries. The external fiber Bragg grating (FBG) sensor is used in this work to collect the signal fluctuations of the wavelength shift in different areas during the Li-ion battery's charge-discharge cycle using a combination of feature analysis and data-driven method, and the characteristics of the wavelength shift and the charge- discharge process in different regions under 125 cycles are statistically analyzed. The reaction degree of Li-ion battery in different areas was compared by wavelength shift value. The wavelength shift factor and charge-discharge process factor related to battery SOH were extracted and trained in the NGO-BP regression model. The experimental results show that the combination of fiber Bragg grating wavelength shift factor and charge-discharge process factor can accurately estimate the SOH of Li-ion battery, and the prediction effect of three areas combination is the best, the goodness of fit R2 is 0.99958, the mean square error MSE is 2.71 × 10−4, the mean absolute error MAE is 1.3373 × 10−2, the root mean square error RMSE is 1.6462 × 10−2, and the mean absolute percentage error MAPE is 1.3679 × 10−2%.The combination of negative electrode and middle area uses fewer sensors to obtain higher prediction accuracy, and its cost performance is the highest. The utilization of fiber Bragg grating wavelength shift factor and charge-discharge process factor has essentially led to the accurate calculation of Li-ion battery SOH, which holds significance for battery health monitoring as well as the design and development of battery management systems.

A Review on Design and Development of Battery Management System for Electric Vehicle

A Review on Design and Development of Battery Management System for Electric Vehicle

SOC estimation of lithium battery based on the combination of electrical parameters and FBG non-electrical parameters and using NGO-BP model

Accurately estimating of the state of charge (SOC) of lithium batteries is of great significance for improving the service life and utilization efficiency of lithium batteries. In this paper, fiber Bragg grating (FBG) sensors are used to capture the offset of the FBG central wavelength caused by strain and temperature during the charging and discharging process of lithium batteries, and the current state of charge of lithium batteries is obtained through the combination of electrical parameters and FBG non-electrical parameters.A battery monitoring experimental system was established, and the comparison of wavelength offset collected by PDMS packaged FBG and bare FBG during the charging and discharging process of lithium batteries was discussed. The prediction results were compared by temperature compensating the central wavelength offset collected during the charging and discharging of lithium batteries, then introduced BP neural network and optimized NGO- BP neural network regression models for training to compare the prediction effects, the results of which point to the fact that in terms of regression model performance and improvement of FBG non-electrical parameters on model performance, the various indicators predicted by NGO-BP are stronger than those predicted by BP. After adding FBG non-electrical parameters, both the BP regression model and the NGO-BP regression model showed remarkable improvement in RMSE, MAE, and R2 compared to the purely electrical parameters, and the best prediction method for the NGO-BP non-purely electrical parameters, with RMSE of 0.0201 and MAE of 0.0154, which decreased by 77.94% and 73.12% respectively compared to the BP pure electrical parameter prediction method, and R2 of 0.9988, which increased by 5.88%, thus the joint accurate estimation of lithium battery SOC by electrical measurement characteristics and FBG non-electrical parameters is basically realized, which brings values for the design and development of battery management system.

Lithium-ion ferrous phosphate prismatic cell aging analysis and assessment for the development of battery management systems

Electric off-road vehicles are gaining popularity as an alternative to fossil fuel-driven vehicles due to their efficiency and eco-friendliness. A series of numerical modelling and experimentation analysis is required to evaluate the trade-off between battery accuracy and life span to identify optimal battery management system design. In this study, investigations were carried out on a lithium-ion ferrous phosphate (LFP) prismatic cell in an electric-powered farm tiller application using the single particle model (SPM) method. The SPM method was used to analyse the battery's behaviour during charge–discharge profiles at 0.5, 1, and 2C ratings. By applying SPM discharging profiles, the Newman, Tiedemann, Gu, and Kim (NTGK) empirical analysis carried out cascaded thermal investigations. The NTGK and experimental studies showed a difference in the error of 0.23 %, 0.38 %, and 0.65 % for the 0.5, 1, and 2C ratings, respectively. Model order reduction (MOR) studies were performed to compute the heat generation rate in a prismatic cell comprising electrode pairs, separators, and current collectors. A parallel study of electrochemical impedance spectroscopy (EIS) experimentation was performed to evaluate the charge transfer resistance analysis for 500, 1000, and 1500 cyclic operations under different depths of discharge (DOD) conditions. The EIS studies assessed charge transfer resistance at maximum DOD conditions for the mentioned cyclic operations were found to be 135, 140, and 164 mΩ.

Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep learning model.

The accurate estimation of battery health conditions is a crucial challenge for development of battery management systems due to the degradation of cathode and anode materials. In this paper, a fusion of deep learning model and feature analysis methods is employed to approach accurate estimation for state of health (SOH) and remaining useful life (RUL). The differential thermal voltammetry (DTV) signal analysis is executed to pre-process the datasets from Oxford University. A deep learning model is constructed with LSTM network as the core, combined with Bayesian optimization and dropout technique. This work shows that the deep learning model could approach the SOH and RUL early estimation with the mean absolute error of RUL maintained around 0.5%. It is potential that this deep learning model, combined with DTV signal analysis methods, could approach early prediction and estimation of battery SOH and RUL, contributing to the development of the next-generation high-energy-density and highly safety commercial batteries.

Nonlinear modeling of lithium-ion battery

The storage of electrical energy represents a major challenge. At present, only capacitors and batteries can have a reserve of an independent energy. Many types of batteries and many electric and chemical factors can affect their performance. Non-linear modeling is the key to using a battery efficiently, it is very necessary to understand its operation, its dynamics and to know the parameters that can affect its performance. In the context of the development of battery management systems and the necessary simulations, a representation of the phenomenon measured is necessary for the form of a model (mathematical, empirical, electrochemical). Lithium-ion batteries are widely used in pure electric vehicles, portable electronic devices, and embedded devices, because of their high specific energy, long life, and low self-discharge rate. This work describes, the nonlinear modeling of lithium-ion batteries using the Hammerstein-Wiener algorithm, we estimate and validate nonlinear models and test several inputs and outputs linearity to have the best results. A pulse discharge test is performed on a 3.7 V lithium-ion battery and with a measured capacity of 2800 mAh to collect data to evaluate those models, results are presented and compared.

Adaptive estimation-based hierarchical model predictive control methodology for battery active equalization topologies: Part I–Balancing strategy

State-of-charge (SOC) balancing is a critical issue for the development of battery management systems for electric vehicles. In the series of two papers, an adaptive estimation-based hierarchical model predictive control (MPC) balancing methodology is proposed to explore different equalization topologies. In the first paper, the superior control for optimal SOC balancing in typical topologies, i.e., dissipative, unidirectional adjacent, bidirectional adjacent, and bus-based, is developed by formulating the proposed MPC. SOC balancing problems of different topologies are modeled by describing the behavior of energy transferring during the charge/discharge process considering practical loss. The MPC balancing strategy is proposed to solve the constrained quadratic optimization by minimizing the balancing time and energy loss. A rule-based fuzzy logic control (FLC) balancing is compared, and in the 5-series cells simulation, the result indicates that the balancing time of MPC and FLC are 292 s and 654 s, respectively. To verify the real-time feasibility of the proposed MPC balancing strategy, a controller hardware-in-the-loop (HIL) test is further implemented. Finally, the influences of bidirectional adjacent and bus-based topologies on equalization performance are discussed, demonstrating a strong correlation between the number of series-connected cells and adopted topology. The proposed MPC-based superior control can be integrated with the later designed low-level adaptive estimation laws driven individual cell equalizer (ICE) controller to comprehensively tackle SOC optimal balancing problems.

A novel extended Kalman filter-based battery internal and surface temperature estimation based on an improved electro-thermal model

The lithium-ion battery temperature monitoring plays a prominent role in developing an appropriate battery thermal management system to ensure battery security, and improve battery performances, and it is also a vital fundamental module for the development of battery management system. To address this issue, this paper proposes a novel estimation procedure for the internal and surface temperatures of a lithium-ion battery cell based on an improved electro-thermal model (IETM). First, an electro-thermal model of lithium-ion battery is built up by combing the temperature-dependent electrical model and the two-state thermal model together. Then, the parameterization scheme of this IETM is carried out to facilitate the real-time estimation of the battery internal and surface temperatures. Furthermore, based on the extended Kalman filter (EKF), the battery IETM is employed to estimate both the battery internal and surface temperatures without requiring extra temperature sensors. Finally, the effectiveness and feasibility of the proposed battery temperature estimation method are demonstrated by performing the simulation and experiment verification.

Modeling and Simulation of Lithium-ion Battery Considering the Effect of Charge-Discharge State

Establishing an accurate battery equivalent model is an important link in the development of battery management system (BMS). The second-order RC equivalent circuit model can accurately describe the dynamic characteristics of the battery. Considering the influence of state of charge (SOC) and battery charging and discharging state on the model parameters, the battery model parameters in charging state and discharging state are identified separately through battery performance test experiments. The equivalent circuit model of lithium ion battery with variable parameters is established by Simscape language, and simulation analysis is carried out. The results show that, considering the influence of battery charge and discharge state, the error of the model is small and the accuracy of the model is improved.

Advances in Prevention of Thermal Runaway in Lithium‐Ion Batteries

The prevention of thermal runaway (TR) in lithium‐ion batteries is vital as the technology is pushed to its limit of power and energy delivery in applications such as electric vehicles. TR and the resulting fire and explosion have been responsible for several high‐profile accidents and product recalls over the past decade. Herein, the causes of TR are described and novel preventative methods are examined, approaching the problem from different angles by altering the internal structure of the battery to undergo thermal shutdown or developing the battery and thermal management systems so that they can detect and prevent TR. Ultimately, a variety of different technologies is needed to address the emerging market of highly specialized lithium‐ion batteries. Key innovations discussed include positive temperature coefficient (PTC) materials, self‐healing polymer electrolytes, and hybrid liquid–solid‐state electrolytes. Mist cooling achieves a highly uniform temperature inside the battery pack without the need for pumps to circulate a coolant. The development of battery management systems (BMSs) which model the internal temperature of the cell from real‐time data and prevent the cell reaching a critical temperature is an essential area for further research.

Fully coupled simplified electrochemical and thermal model for series-parallel configured battery pack

Battery packs are often designed with multiple battery cells configured in series and/or parallel combinations to meet the energy and/or power requirements of target applications. Modeling of these battery packs is very complex, computationally challenging and requires extending a single cell model to multi-cell models including electrical connections between cells. Also, the cell-to-cell variations of capacity, resistance, and temperature in the pack are vital in the battery pack design and battery management system development. Therefore, a robust modeling platform with in-built physics and fully coupled models both at the cell and the pack level is developed. The modeling framework consists of Simplified Electrochemical and lumped Thermal Model (SEM-T) at the cell level and Equivalent-Circuit Model (ECM) for Ohmic calculations and natural Convective Thermal Model (CTM) for thermal distribution at the pack level. A 7S4P battery pack with 21700 NCA cylindrical cells is considered in this study and the model is validated with the experimental data sets at different c-rates and temperatures. The developed model is able to successfully predict the pack voltage, capacities, and temperatures. The proposed model is computationally efficient and can be easily adopted for on-board implementation in battery management system for safe and reliable operations.

Measurement Chamber Design for Testing Batteries of the Electric Vehicles

Crucial factors with respect to modern autonomous vehicles include reliability and design. Researchers and engineers strive to increase the number of vehicles over the latest possibilities both in industrial and in military applications. A number of modern batteries are available on the market for the electric autonomous vehicles. The authors suggest the use of test chambers to investigate optimal battery use and performance in vehicles. The results of the theoretical research suggest that the use of test chambers during battery management system development is necessary.

A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction

Battery modeling plays an important role in remaining range prediction and battery management system development. An accurate and realistic battery model is essential to design an efficient electric storage system. The goal of this paper is to investigate the performance of different circuit topologies for diffusion process in the equivalent circuit models (ECMs). The theory of diffusion process approximation by using resistive-capacitor (RC) networks is explained in frequency domain. The terminal voltage predictive capabilities of the ECMs are compared and validated with test data. The numerical simulation results show that model prediction accuracy and computation burdens increase along with the number of RC pairs. The ECM with three RC networks is the best choice in terms of the balance between accuracy and complexity for ternary lithium batteries. In addition, a novel method of combining unscented Kalman filter (UKF) algorithm with initial state of charge (SOC) acceleration convergence for SOC estimation is proposed. The results of urban dynamometer driving schedule (UDDS) show that ECM with three RC networks has the best comprehensive performance on calculation cost and SOC estimation accuracy.

Design and development of battery management system (BMS) using hybrid multilevel converter

ABSTRACTBatteries that are integrated to the renewable system have to bear a wide range of operational conditions such as varying rates of charge/discharge, depth of discharges, temperature fluctuations and so on. When these batteries are connected in series, after several charging and discharging cycles, the voltage and state-of-charge (SOC) imbalance will occur between different batteries. In the proposed topology, the hybrid multilevel converter is used along with auxiliary battery to get constant output voltage during discharging and store excess power during charging. As a result, the voltage and SOC get balanced and also overcharging and discharging can be avoided. The multilevel output is used to improve the performance of the load and battery lifetime.

A Technical Review of BMS Performance Standard for Electric Vehicle Applications in Indonesia

The development of Battery Management System (BMS) standards in Indonesia has been carried out causing the FACTS approach. That's approach makes it possible to accommodate all stakeholder requirements. However, the approach has not yet considered a technical review of the regulated standards. Based on this, this study undertook a comprehensive review of BMS performance parameters set out in the standard. In order that the regulated standards not only accommodate the needs of stakeholders but also consider BMS technical studies in order not to impede the future development of BMS.

Modelling electro-thermal response of lithium-ion batteries from normal to abuse conditions

Insight of thermal behaviour of lithium-ion batteries under various operating conditions is crucial for the development of battery management system (BMS). Although battery thermal behaviour has been studied by published models, the reported modelling normally addresses either normal operation or thermal runaway condition. A comprehensive electro-thermal model which can capture heat generation, voltage and current variation during the whole process from normal cycling to thermal runaway should be of benefit for BMS by evaluating critical factors influencing potential transition to thermal runaway and investigating the evolution process under different cooling and environment conditions. In this study, such a three-dimensional model has been developed within the frame of open source computational fluid dynamics (CFD) code OpenFOAM to study the electrical and thermal behaviour of lithium-ion batteries (LIBs). The equations governing the electric conduction are coupled with heat transfer and energy balance within the cell. Published and new laboratory data for LiNi0.33Co0.33Mn0.33O2/Li1.33Ti1.67O4 (LNCMO/LTO) cells from normal cycling to thermal runaway have been used to provide input parameters as well as model validation. The model has well captured the evolution process of a cell from normal cycling to abnormal behaviour until thermal runaway and achieved reasonably good agreement with the measurements. The validated model has then been used to conduct parametric studies of this particular type of LIB by evaluating the effects of discharging current rates, airflow quantities, ambient temperatures and thickness of airflow channel on the response of the cell. Faster function losses, earlier thermal runaway and higher extreme temperatures were found when cells were discharged under higher current rates. The airflow with specific velocity was found to provide effective mitigation against over-heating when the ambient temperature was below 370K but less effective when the ambient temperature was higher than the critical value of 425K. The thickness of airflow channel was also found to have critical influence on the cell tolerance to elevated temperatures. These parametric studies demonstrate that the model can be used to predict potential LIB transition to thermal runaway under various conditions and aid BMS.

Development of battery management systems (BMS) for electric vehicles (EVs) in Malaysia

Battery Management Systems (BMS) is an electronic devices component, which is a vital fundamental device connected between the charger and the battery of the hybrid or electric vehicle (EV) systems. Thus, BMS significantly enable for safety protection and reliable battery management by performing of monitoring charge control, state evaluation, reporting the data and functionalities cell balancing. To date, 97.1% of Malaysian CO 2 emissions are mainly caused by transportation activities and the numbers will keep rising as numbers of registered car increase close up to 1 million yearly; double the amounts in the last two decades. The uncertainty of a battery’s performance poses a challenge to predict the extended range of EVs, which need BMS implementation of optimization of optimum power management. Hence, using MATLAB/SIMULINK software is one of the potential methods of BMS optimization with power generated by Hybrid Energy Storage system of lithium-ion battery. Therefore, this paper address through reviewing previous literatures initially focuses on the BMS optimization for EVs (car) in Malaysia as prognostic technology model improvement on performance management of EVs.

바이모달트램용 LPB Management System 개발 및 적용

Bimodal Tram developed by KRRI is driven by a series Hybrid propulsion system which has both the CNG engine, generator and LPB(Lithium Polymer Battery) pack. It has three driving modes; Hybrid mode, Engine mode and Battery mode. Even in case of Battery mode, LPB pack to get enough power to drive the vehicle only by itself onsists of 168 LPB cells(80Ah per lcell), 650V. It is important thing to manage LPB pack in a right way, which will extend the lifetime of LPB cells and operate in the hybrid mode effectively. This paper has shown the development of battery management system(12 BMS, 1 BMS per 14cells) to manage LPB pack which is connected with CAN(Controller Area Network) each other and measure the voltage, current, temperature and also control the cooling fan inside of LPB pack. Using the measured data, BMS can show the SOC(State of Charge), SOH(State of Health) and other status of LPB pack including of the cell balancing.

Comparison of Prognostic Algorithms Based on PF and EKF for Estimating Remaining Useful Life of Li-Ion Batteries

To estimate remaining useful life (RUL) of Li-ion batteries is a key factor for correct and safe battery management, particularly for the development of Battery Management System (BMS). A lumped parameter model which integrates the non-linear open-circuit voltage, current, temperature, cycle number, and remaining capacity and other dynamic characteristics is created based on the battery electrical characteristics is presented. A particle filter (PF) algorithm which syncretizes Li-ion battery electrochemical working process is proposed according to the sequence importance of re-sampling to predict its discharge end time in single cycle time and cycle life. Besides, for comparison, a extended Kalman filter (EKF) algorithm is also proposed to estimate the RUL based on the same statistics. Simulation results show that the PF algorithm according to lumped parameter model has a better precision in estimating RUL compared with the EKF algorithm.

A Study on the Testing System and Method for Li-Ion Power Battery

The shortage of energy prompts Li-ion battery to become the new alternative energy. In order to master battery characteristics, solve the battery safety problems and prolong service life, it is necessary to research essentially the battery performance, cycle life and capacity, which can provide the basis for the development of battery management system. In this paper, we build a set of Li-ion power battery testing system of charging and discharging according to the existing equipment, test the battery pack through a combination of the hardware and software, and put forward a set of complete testing method. Then on the basis of the test data and the relation curve between Li-ion power battery capacity and cycle times, we have proved that the testing system and testing method are effective.